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add a naive DDP for model interface (#78)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Laughing
2022-12-19 13:24:39 +08:00
committed by GitHub
parent 48c95ba083
commit 7690cae2fc
4 changed files with 94 additions and 27 deletions
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20
ultralytics/yolo/v8/detect/val.py
View File

@ -25,11 +25,8 @@ class DetectionValidator(BaseValidator):
self.class_map = None
self.targets = None
self.metrics = DetMetrics(save_dir=self.save_dir, plot=self.args.plots)
self.iouv = torch.linspace(0.5, 0.95, 10, device=self.device) # iou vector for mAP@0.5:0.95
self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95
self.niou = self.iouv.numel()
self.seen = 0
self.jdict = []
self.stats = []
def preprocess(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True)
@ -56,6 +53,9 @@ class DetectionValidator(BaseValidator):
self.names = dict(enumerate(self.names))
self.metrics.names = self.names
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.seen = 0
self.jdict = []
self.stats = []
def get_desc(self):
return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)")
@ -98,7 +98,7 @@ class DetectionValidator(BaseValidator):
tbox = ops.xywh2xyxy(labels[:, 1:5]) # target boxes
ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct_bboxes = self._process_batch(predn, labelsn, self.iouv)
correct_bboxes = self._process_batch(predn, labelsn)
# TODO: maybe remove these `self.` arguments as they already are member variable
if self.args.plots:
self.confusion_matrix.process_batch(predn, labelsn)
@ -139,7 +139,7 @@ class DetectionValidator(BaseValidator):
if self.args.plots:
self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values()))
def _process_batch(self, detections, labels, iouv):
def _process_batch(self, detections, labels):
"""
Return correct prediction matrix
Arguments:
@ -149,10 +149,10 @@ class DetectionValidator(BaseValidator):
correct (array[N, 10]), for 10 IoU levels
"""
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
@ -162,7 +162,7 @@ class DetectionValidator(BaseValidator):
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
return torch.tensor(correct, dtype=torch.bool, device=detections.device)
def get_dataloader(self, dataset_path, batch_size):
# TODO: manage splits differently

18
ultralytics/yolo/v8/segment/val.py
View File

@ -5,13 +5,11 @@ import numpy as np
import torch
import torch.nn.functional as F
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.utils import ops
from ultralytics.yolo.utils.checks import check_requirements
from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
from ultralytics.yolo.utils.torch_utils import de_parallel
from ..detect import DetectionValidator
@ -55,6 +53,9 @@ class SegmentationValidator(DetectionValidator):
self.metrics.names = self.names
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.plot_masks = []
self.seen = 0
self.jdict = []
self.stats = []
def get_desc(self):
return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
@ -106,11 +107,10 @@ class SegmentationValidator(DetectionValidator):
tbox = ops.xywh2xyxy(labels[:, 1:5]) # target boxes
ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct_bboxes = self._process_batch(predn, labelsn, self.iouv)
correct_bboxes = self._process_batch(predn, labelsn)
# TODO: maybe remove these `self.` arguments as they already are member variable
correct_masks = self._process_batch(predn,
labelsn,
self.iouv,
pred_masks,
gt_masks,
overlap=self.args.overlap_mask,
@ -135,7 +135,7 @@ class SegmentationValidator(DetectionValidator):
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
'''
def _process_batch(self, detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
def _process_batch(self, detections, labels, pred_masks=None, gt_masks=None, overlap=False, masks=False):
"""
Return correct prediction matrix
Arguments:
@ -157,10 +157,10 @@ class SegmentationValidator(DetectionValidator):
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
@ -170,7 +170,7 @@ class SegmentationValidator(DetectionValidator):
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
return torch.tensor(correct, dtype=torch.bool, device=detections.device)
# TODO: probably add this to class Metrics
@property